Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach
Darbandi, Hamed; Serra Braganca, F.M.; van der Zwaag, Berend Jan; voskamp, john P.; Imogen Gmel, Annik; Halla Haraldsdóttir, Eyrun; Havinga, Paul
(2021) ACS Sensors, volume 21, issue 3, pp. 1 - 12
(Article)
Abstract
Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome
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these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.
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Keywords: Breed, Feature extraction, Gait, Inertial measurement unit, Machine learning, Analytical Chemistry, Biochemistry, Atomic and Molecular Physics, and Optics, Instrumentation, Electrical and Electronic Engineering
ISSN: 1424-3210
Publisher: Multidisciplinary Digital Publishing Institute
Note: Funding Information: This study was partly funded by EFRO OP-Oost (project ?Paardensprong?), and the Swiss Federal Office for Agriculture (contract number 627001325). The authors wish to thank Sigridur Bjornsdottir and V?kingur Gunnarsson from H?lar University College, Elin Hernlund and Marie Rhodin from Swedish University of Agricultural Sciences, Michael Weishaupt and Marie-Theres Dittmann from University of Zurich, and all the animal caretakers that helped with the data collection. Funding Information: Funding: This study was partly funded by EFRO OP-Oost (project “Paardensprong”), and the Swiss Federal Office for Agriculture (contract number 627001325). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
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